Fecal Microbiota of Cats With Naturally Occurring Chronic Diarrhea

8/9/2019 Fecal Microbiota of Cats With Naturally Occurring Chronic Diarrhea

1/7

F e c a l M i c r o b i o t a o f Ca t s w i t h N a t u r a l l y O c c u r r i n g Ch r o n i cD i a r r h e a As s e s s e d U s i n g 1 6 S r RN A G e n e 4 5 4 - P y r o se q u e n c i n g

b e f o r e a n d a f t e r D i e t a r y T r e a t m e n t

Z. Ramadan, H. Xu, D. Laflamme, G. Czarnecki-Maulden, Q.J. Li, J. Labuda, and B. Bourqui

Background: The gastrointestinal (GI) microbiota has a strong impact on the health of cats and these populations can bealtered in GI disease. Little research has been done to associate improvement in diarrhea with changes in GI microbiota.

Objective: To evaluate GI microbiota changes associated with diet change and related improvement in diarrhea in cats

with chronic naturally occurring diarrhea.

Animals: Fifteen adult Domestic Shorthair cats with naturally occurring chronic diarrhea.

Methods: Controlled crossover dietary trial for management of diarrhea. Fecal microbiome was assessed using 454-py-

rosequencing. Relationships among fecal score (FS), diet, and microbiome were explored using partial least square method,

partial least square method discriminant analysis, and orthogonal partial least square method with discriminant analysis

(OPLS-DA).

Results: Dominant bacterial phyla included the Firmicutes and Bacteroidetes, followed by Fusobacteria, Proteobacte-

ria, Tenericutes, and Actinobacteria. Orthogonal partial least squares (OPLS-DA) clustering showed significant microbial

differences within cats when fed Diet X versus Diet Y, and with Diet Y versus baseline. Significant correlations were found

between the microbiome and FSs. Those bacteria with the strongest correlation with FS included Coriobacteriaceae Slac-

kia spp., Campylobacter upsaliensis, Enterobacteriaceae Raoultella spp., Coriobacteriaceae Collinsella spp., and bacteria of

unidentified genera within the families of Clostridiales Lachnospiracea and Aeromonadales Succinivibrionacease, suggest-

ing that increased numbers of these organisms may be important to gut health.Conclusions and Clinical Importance: Alterations in intestinal microbiota were associated with improvement in diarrhea,

but, from our data we cannot conclude if changes in the microbiome caused the improvement in diarrhea, or vice versa.

Key words: Gastroenterology; Nutrition.

The gastrointestinal (GI) microbiota has a strongimpact on the health of cats and dogs,1 and themicrobiome can be altered in GI disease.24 Priorresearch has confirmed alterations in intestinal micro-biota in cats with GI disease.5,6 Documented changes

include increases in Clostridium spp., and decreases in

Bidifobacteriumspp., Lactobacillusspp. andBacteriodes

spp.,68

all of which may be influenced by dietary char-acteristics including fermentable fiber.3,913 Studies have

shown that dietary changes can result in clinicalimprovement in diarrhea in cats and dogs.1420 In other-

wise healthy dogs, dietary-induced changes were associ-ated with altered microbiota, altered fecal quality, or

both.21,22 However, comprehensive studies are lackingto determine if clinical improvement in cats with diar-

rhea is associated with changes in the GI microbiome.Metagenomics is the analysis of genomic patterns of

entire communities of microbes, such as the intestinal

microbiome. The ability to perform such analyses using

454-pyrosequencing, which allows accurate and quanti-tative analysis of DNA, provides a more comprehensive

view of the microbiome without the limitations of cul-ture methods.23,24 Although there is evidence that the

intestinal microbiota differs along the GI tract, fecalsamples are more readily available for clinical studies,

and alterations in fecal microflora have been shown tooccur in cats with diarrhea.6,25,26 In this study, 16SrRNA sequence data were analyzed using 454-pyro-

sequencing to characterize the fecal microbiome in cats

with chronic diarrhea before and after response to die-tary treatment. Our objectives were to characterize thephylogeny of the feline fecal microbiome, assess the

changes induced by the therapeutic diets, and to corre-late these microbial community changes with clinical

improvement in diarrhea assessed by fecal scores (FS).

Materials and Methods

Animals

Freshly voided fecal samples were collected from adult

Domestic Shorthair cats undergoing a controlled, crossover

From the Nestle Research Center, St. Louis, MO (Ramadan,Laflamme, Czarnecki-Maulden, Li, Labuda); the Nestle Purina

Product Technology Center, St. Louis, MO (Xu); and the Nestle

Product Technology Center, Konolfingen, Switzerland (Bourqui).

This research was conducted at the NestlePurina Pet Care Center

in Missouri and the Nestle Research Center in St. Louis, MO

between 2008 and 2009. This study was presented in abstract form

at the 2013 ACVIM Forum, Seattle, WA.

Corresponding author: Dr Ziad Ramadan, Nestle Research

Center, Checkerboard Square 2S, St. Louis, MO 63164; e-mail:

[email protected].

Submitted August 16, 2013; Revised October 8, 2013;

Accepted October 24, 2013.Copyright 2013 by the American College of Veterinary Internal

Medicine

10.1111/jvim.12261

Abbreviations:

FS fecal score

GI gastrointestinal

OPLS-DA orthogonal partial least square method with

discriminant analysis

OTU operational taxonomic unit

PCA principal components analysisPLS-DA partial least square method discriminant analysis

PLS partial least squar e method

J Vet Intern Med2014;28:5965


2/7

clinical trial for the dietary management of chronic diarrhea. The

clinical design and other aspects of this study have been previ-

ously reported.20 Briefly, cats with naturally occurring diarrhea

lasting at least 3 months were identified among cats at the Nestle

Purina Pet Care Center in Missouri, USA. Diarrhea was defined

as a FS of 6 or 7 using a 7-point scoring system where

1 = extremely dry and firm, 23 = normal stools, and 7 = very

watery.20 Cats were excluded from the study if they had received

any treatment for diarrhea in the 6 weeks before initiation of the

study or if they had evidence of intestinal parasites, infectious

disease, or a systemic disease that may cause diarrhea.

Study Design

The study protocol was approved by the Nestle Purina Institu-

tional Animal Care and Use Committee. Sixteen cats were

selected and were individually housed. Once cats were assigned

to housing, the units were arbitrarily divided into 2 equal-sized

groups. In order to minimize effects caused by differences in the

cats previous diets and to adapt all cats to a canned food diet,

all cats entered into the study were fed the same canned mainte-

nance dieta during a 2-week baseline period (Baseline). This per-

iod was considered sufficient because prior studies documentedthat if cats with diarrhea respond to dietary change, they usually

do so within 12 weeks.15,16,19 Two cats refused to eat the canned

diet and were replaced during the baseline period. One group of

cats then was fed Diet Xb whereas the other group was fed Diet

Yc as their sole diet for 1 month (Period 1), after which they

were switched to the alternate diet for an additional month (Per-

iod 2). Cats were individually fed once daily based on daily

energy requirements and water was available ad libitum. Data

collection occurred during the last week on each diet. FSs were

assigned daily by 1 of 3 technicians trained in fecal scoring, with

each feces being scored separately. During each of the last 3 days

of each period, freshly voided fecal samples were collected, mixed

with 10% w/w glycerol, flushed with CO2, and frozen at 80C

until analysis. Collection and processing of fecal samples were

performed within 15 min of defecation.

Extraction of DNA and Metagenomic454-Pyrosequencing

Genomic DNA was extracted from fecal samples using the

method of Tsai and Olson.27 Briefly, fecal samples were thawed

in ice, weighed and suspended in phosphate-buffered saline

(0.85% NaCl, 120 mM NaH2PO4, pH = 8.0) then centrifuged at

5525 9 g for 10 min. After discarding the supernatant, the pellets

were resuspended in 0.5 mL lysis solution (0.15 M NaCl, 0.1 M

EDTA, pH = 8.0) containing 15 mg/mL lysozyme and incubated

for 30 min with constant agitation in a 37C shaking water bath

before, and another 30 min after, addition of 0.5 mL of sodium

Tris sodium-dedocyl-sulfate solution (0.1 M NaCl, 0.48 M TrisHCl [pH = 8.0], 10% sodium-dedocyl-sulfate, pH = 8.0). Three

freeze-thaw cycles were performed to break open the bacterial

cell walls. After the third thaw, Proteinase Kd was added to each

sample to a final concentration of 50 lg/mL and samples were

again incubated in a 37C shaking water bath for 30 min with

constant agitation, then centrifuged at 17,500 9 g for 20 min in

a refrigerated microcentrifuge. Without disturbing the pellet,

850 lL of the supernatant was removed and placed into a clean

tube on ice. Samples remained on ice for the remainder of the

processing. Samples were mixed with an equal volume of phenol

(pH = 7.9), then centrifuged for 6 min at 16,500 9 g . An equal

volume of phenol/chloroform/isoamyl alcohol (25 : 24 : 1) was

mixed with 700 lL supernatant, then again centrifuged for 6 min

at 16,500 9 g. This supernatant (550 lL) was mixed with an

equal volume of chloroform/isoamyl alcohol (24 : 1) then centri-

fuged at 16,500 9 g for 6 min. A 400 lL aliquot of this superna-

tant was mixed with 400 lL ice cold isopropanol and 96 lL

10.5 M ammonium acetate (1.125 M final concentration), then

frozen at 80C freezer overnight or until processing (up to

2 weeks).

Samples were thawed on ice for ~510 min before further pro-

cessing. DNA pellets were obtained by centrifuging at

17,500 9 g for 20 min in a refrigerated microcentrifuge. The

supernatant was removed and pellets were washed with 500 lL

70% ethanol, then dried. The pellets were resuspended in 200 lL

Tris-EDTA buffer (10 mM Tris HCl [pH = 8.0], 1 mM EDTA,

pH = 8.0). DNA was quantified by Quant-It.e All samples were

then stored at 80C until further analysis.

Metagenomic 16S rRNA pyrosequencing was performed by

Core for Applied Genomics and Ecology.f The V1V2 region of

the 16S rRNA gene was amplified using bar-coded fusion primers

with the Roche-454 A or B titanium sequencing adapters, fol-

lowed by a unique 8-base barcode sequence (B) and finally the 5

ends of primer A-8FM (5-CCATCTCATCCCTGCGTGTC

TCCGACTCAGBBBBBBBBAGAGTTTGATCMTGGCTCAG)

or primer B-357R (5-CCTATCCCCTGTGTGCCTTGGCAGT

CTCAGBBBBBBBBCTGCTGCCTYCCGTA-3). All PCR reac-tions were quality controlled for amplicon saturation by gel elec-

trophoresis; band intensity was quantified against standards

using GeneToolsg image analysis software. For each region of a

2-region picotiter plate, amplicons from 48 reactions were pooled

in equal amounts and gel purified. The resulting products were

quantified using PicoGreenh and a Qubit fluorometeri before

sequencing using a Roche-454 GS-FLX Pyrosequencer.j

Data Processing Pipeline

The raw data from 454-pyrosequencing were processed using

QIIME.28 Data were filtered to remove low-quality reads not

meeting the following quality criteria: (1) a complete barcode

sequence immediately followed by a forward primer sequence,

with no mismatch in either barcode or primer sequence; (2) read

lengths between 200 and 1,000 bases; (3) average quality score of

25 or higher in a sliding window of 50 bases; and (4) maximum

homopolymer run of 6. Processed reads were then demultiplexed

into barcode-indexed sample categories. The barcode, forward

primer, and reverse primer were subsequently trimmed from each

read. This yielded a total of 556,366 reads from 48 samples with

an average of 11,591 reads per sample. The average length for

the reads was 358 bases.

Reads were clustered into operational taxonomic units (OTU)

using a reference-based UCLUST algorithm at a 97% sequence

similarity level.29 The reference data file was obtained from the

greengenes website (\http://greengenes.lbl.gov, 2011 release).

A consensus taxonomic lineage was assigned to each OTU using

the ribosomal database project (RDP) nave Bayesian classifier

30

at a minimum confidence interval of 0.8. The RDP classifier was

retrained using the greengenes taxonomy. Finally, an OTU table

was constructed with proper taxonomic identifications for each

OTU.

Statistical Data Analysis

Mean fecal scores (FSm) for each cat were determined by aver-

aging all of that cats scores recorded during the last 7 days of

each study period. FSs were not affected by feeding order

(period), so data from both periods were pooled. The relation-

ships between FSm, diet and microbiome were explored using

partial least square method (PLS), partial least square method

discriminant analysis (PLS-DA), and orthogonal partial least

60 Ramadan et al


3/7

square method with discriminant analysis (OPLS-DA) using

SIMCA-P+k and MATLABl routines. PLS is a chemometrics

method used to measure quantitative relationships between 2

data sets where both are matrices, usually comprising spectral

data such as calibration samples (X), and a set of quantitative

values (Y). For PLS-DA, the Y matrix contains qualitative val-

ues (eg, presence of diarrhea or treatment group). OPLS-DA is a

multivariate method used to eliminate extraneous variance from

the X data matrix that is unrelated to class, in this case, FSs.

Having removed the extraneous variation (eg, intersubject age,

sex), the OPLS-DA models enhance the predictive ability of the

model and simplify interpretation.31,32 A standard 7-fold cross-

validation method was applied to establish the robustness of the

models. In OPLS models, R2X, R2Y , a nd Q2 are reported.

The values of R2X and R2Y show how much of the variation in

the datasets X and Y, respectively, are explained by the model.

The cross-validation parameter, Q2 (which can range from 1 to

+1), represents the predictability of the models and is used to test

the validity of the model against overfitting. A positive Q2 value

indicates that differences between groups are statistically signi-

ficant, whereas a negative Q2 value indicates no significant

relationship among the data.

Principal components analysis (PCA) and pairwise discrimi-nant analysis were applied to sample classes (diets) with unit vari-

ance scaling (each parameter has a mean of 0 and a variance of

1). This approach filters out metagenomic information that is not

correlated with the predefined classes whereas the loadings yield

information on which bacterial signals are associated with the

observed clustering, thus giving a means for metagenomics inter-

pretation. Pairwise OPLS-DA models were generated with 1 pre-

dictive component, and 2 orthogonal components to discriminate

among the 3 diets. Models were calculated using family, genus,

and species levels. OPLS-DA plots were generated only to species

level.

Results

One cat was withdrawn from the study for unrelatedmedical reasons. Fifteen cats (13 neutered males,

2 spayed females; mean age, 10 years [range,617 years]) completed all phases of the study, as pre-

viously described.20 Most cats had signs consistentwith either large bowel (n = 7) or mixed large andsmall bowel (n = 7) diarrhea. Mean FS improved

(P < .01) from baseline (5.5 0.3) after both Diet X

(5.0 0.4 and 4.4 0.4 for periods 1 and 2, respec-tively) and Diet Y (4.0 0.6 and 4.2 0.5 for periods

1 and 2, respectively), with significantly greaterimprovement after Diet Y (P < .01). Changes in FS inresponse to diet were not affected by the order in

which the diets were fed (P = .65). Individual FSimproved at least 1 unit in 40% of the cats while fedDiet X, and in 67% of the cats while fed Diet Y,resulting in normal stools (FS 3) in 13.3% of cats

fed Diet X and in 46.7% of cats fed Diet Y. 20

Pyrosequencing of fecal samples detected 8 phyla, 14

classes, 25 orders, 47 families, 96 genera, and 146species of bacteria. Firmicutes, Bacteroidetes, Fusobac-

teria, Proteobacteria, Tenericutes, and Actinobacteriawere the most abundant phyla in these cats (Table 1).

The most prevalent bacterial classes were Bacilli andClostridia among the Firmicutes and Bacteroidia and

Flavobacteria among the Bacteroidetes. Prevotella was

the most abundant genus in fecal samples regardless of

diet consumed, with mean of 24% across all samples,followed by Fusobacterium (9%), unclassified Fusobac-teriaceae (9%), Clostridium (8%), and Streptococcus

(6%).The OPLS-DA models showed significant differencesin the fecal microbiome of cats when fed Diet Y versus

baseline at the family (Q2 = 0.126), genus (Q2 = 0.399),and species (Q2 = 0.61, Fig 1A) level. Likewise, signifi-cant but smaller differences were noted between cats

fed Diet X versus Diet Y at the family (Q2 = 0.0588),genus (Q2 = 0.127), and species (Q2 = 0.197, Fig 1B)

levels. There were no differences after Diet X com-

pared to baseline. OPLS-DA clustering showed thegreatest microbial differences in cats when fed Diet Yversus baseline (Q2 = 0.61).

Significant changes in bacterial populations occurredin cats fed diet Y versus baseline or diet X (Fig 2).

For example, the species Clostridium perfringens,Prevotella copri, Plesiomonas shigelloides, Helicobacter

cinaedi, Lactobacillus helveticus, and Bacteroides fragi-lis and others were decreased and Ruminococcus gna-

vus, Streptococcus suis and Eubacterium dolichum wereincreased in cats fed diet Y compared to baseline.

Other unidentified species in various genera also chan-ged in cats fed diet Y. Some of these alterations also

were found with diet X but not to the same extent,consistent with the observation that Diet X was inter-mediate to Diet Y regarding improvement in FS.

Using OPLS-based evaluations, significant correla-

tions between microbiome and FS were found for cats

fed Diet Y at the genus (Q2 = 0.464) and species(Q2 = 0.115) levels, as well as for cats fed Diet X at

family (Q2 = 0.232), genus (Q2 = 0.736), and species(Q2 = 0.717) levels. Prediction plots generated from

the OPLS data at the species level, wherein FSs were

predicted based on the microbiome, showed strongcorrelations between predicted and actual FS (r2 = 1.0and 0.6, P < .005, for Diets X and Diet Y, respec-

tively). OPLS regression identified 63 different bacteriawith significant negative or positive correlations

(r > 0.2; P .05) to FS in cats consuming Diets Xor Y, although they differed between diets (Table S1:

on-line supplement) Those organisms most strongly

correlated (r > 0.70) with FS were within cats while

Table 1. The phylum level composition of the fecalmicrobiota in cats with chronic diarrhea fed different

diets. This analysis was computed using both V1V2regions.

PhylumTotal Baseline Diet X Diet Y

Percent of bacteria

Firmicutes 34.34 33.72 35.65 33.82

Bacteroidetes 30.05 33.54 30.16 26.13

Fusobacteria 18.81 14.66 16.57 25.43

Proteobacteria 7.66 9.14 7.52 6.16

Tenericutes 6.56 6.21 7.45 6.12

Actinobacteria 2.57 2.72 2.65 2.33

Cyanobacteria 0.0034 0.0036 0.006 0.0008

TM7 0.0003 0.0007 0 0

Microbiota in Feline Diarrhea 61


4/7

fed Diet Y, and included Coriobacteriaceae Slackiaspp. (r = 0.83), Campylobacter upsaliensis (r =0.79), Enterobacteriaceae Raoultella spp. (r = 0.72),and bacteria of unidentified genera within the familiesof Clostridiales Lachnospiracea (r = 0.76), and Aero-

monadales Succinivibrionacease (r = 0.71).

Discussion

To our knowledge, this study is one of the first to

apply next generation pyrosequencing to character-ize the hindgut microbiome of cats with chronic diar-

rhea, and to show changes in the microbiome withimprovement in diarrhea associated with dietary man-

agement. The data showed strong correlations betweenFS after consumption of therapeutic diets and several

organisms, generating a model that accurately pre-dicted the FS of these cats based on the microbiota.

Those bacteria with the strongest correlation with

FS-included Coriobacteriaceae Slackia spp., Campylo-bacter upsaliensis, Enterobacteriaceae Raoultella spp.,Coriobacteriaceae Collinsella spp., and bacteria of

unidentified genera within the families of ClostridialesLachnospiracea and Aeromonadales Succinivibriona-

cease, suggesting that increased numbers of theseorganisms may be important to gut health.

In this study, we detected the presence of 8 bacterialphyla. Dominant bacterial phyla included the Firmi-

cutes and Bacteroidetes, both of which comprised 3034% of all sequences. Firmicutes includes, among

many others, the commonly recognized genera Lacto-bacillus, Clostridium, and Enterococcus, whereasBacteroidetes includes Helicobacteria, Escherichia,

Pasturella, and Pseudomonas among its genera. These

were followed in abundance by Fusobacteria (18.8%),Proteobacteria (7.7%), Tenericutes (6.6%), and

Actinobacteria (2.6%). The proportions were similaracross all diets with the exception that Fusobacteriawere higher in cats fed Diet Y. Although the specific

abundance of Firmicutes in this study is less, and theabundance of Bacteroidetes and Tenericutes is morethan reported by others, our results are consistent with

most other studies using nonculture methods of evalu-ation, indicating that the dominant phyla in cat fecesare Firmicutes, Bacteriodetes, Proteobacteria, Fuso-

bacteria, and Actinobacteria.2426,33,34 In contrast, Tunet al. reported that Bacteroidetes were the most preva-

lent in cats (68%), followed by Firmicutes (13%) andProteobacteria (6%).35 The reported differences in spe-

cific abundance among these phyla may be related todifferences in diets consumed, or differences in meth-

ods and the use of bacterial primers that target certainhypervariable regions of the 16S rRNA. The selection

of bacterial primers of the 16S rRNA is an importantfactor in any metagenomics study because some prim-

ers could underestimate or overestimate certain classesof bacteria.24,26,36 The large number of Tenericutes rel-

ative to prior papers may be related to recent changesin taxonomy, with many genera in the Tenericutes

phylum previously classified as Firmicutes.37,38 How-ever, the differences between results from this studyand prior studies may reflect true differences in bacte-

rial populations associated with naturally occurringchronic diarrhea.

The microbiota of cats at baseline with active diar-

rhea and of cats after being fed Diet X with partial res-

olution of diarrhea were very similar. However,samples collected after Diet Y, which was associated

with greater improvement in diarrhea, showed signifi-cant differences compared to baseline and compared to

Diet X. Mostly, these changes were in the same direc-tion; ie, the abundance was increased or decreased

compared to both baseline and Diet X. Among the fewexceptions, Eubacterium spp., Enterococcus spp. and

Streptococcus suis all were increased after Diet Y com-pared to baseline but decreased compared to Diet X.

Many of the organisms frequently identified usingculture methods, such as Lactobacillus spp., Bifidobac-

terum, C. perfringens, and Escherichia coli, showed

weak or no correlation with fecal quality in this study.

A

B

Fig 1. Orthogonal partial least square with discriminant analysis

(OPLS-DA) plot, showing results of multivariate analysis of the

effects of dietary changes on fecal microbiota at the species level.

Data were visualized by means of component scores plots, where

each point represents an individual metagenomic profile of a

sample. The score matrix (tcv and to) represent projections onto

the latent variables of the OPLS-DA model. (A) Scores plot

showing significant differences (P < .01) between Diet Y versus

Baseline; and (B) Scores plot showing significant differences

(P < .01) between Diet Y versus Diet X.

62 Ramadan et al


5/7

C. perfringens, however, was significantly decreased incats fed Diet Y compared to baseline and compared to

Diet X. Previous work had shown a positive correla-

tion between increased dietary protein and Clostridium,including C. perfringens.13,39 In this study, Diet Y con-tained more protein than Diet X, so factors other than

dietary protein also affect fecal C. perfringens. SomeLactobacillus species, such as L. helveticus, also were

increased in cats fed Diet Y. Desulfovibrio spp., anorganism previously shown to be increased in cats with

inflammatory bowel disease,6 also was increased in catsfed Diet Y compared to baseline and Diet X.

However, there was no correlation between this organ-ism and FS in the cats in this study.

There were some limitations to this study. It was a

clinical study evaluating cats with chronic, naturallyoccurring diarrhea, and only 15 cats were available forthe study. The use of a crossover design increased the

power of the study and helped overcome the inherentvariation in individual differences in microbiota. Cats

were given 3 weeks to adapt to each diet before begin-ning the sampling period, which previously has been

documented as sufficient for a stable response to die-tary change in cats with diarrhea,15,16,19 but there was

Fig 2. Heat map plot of significant bacterial microbiota. The heat plot (r range from1 to +1) indicates the abundance of bacteria that

were up- or down-regulated in cats after eating each diet. Red corresponds to bacteria that are up-regulated in Diet Y (high positive r

correlation value) and green corresponds to bacteria that are down-regulated in Diet Y (high negative r correlation value) which resulted

from the OPLS-DA model. The bacterial genera and species were grouped according to their phylum level.



6/7

not a washout or return to baseline between dietarytreatments. When planning the study, it was assumedthat there would be no carryover effect because cats

with diet-responsive diarrhea usually respond within12 weeks and a 3-week adaptation time during each

phase was allowed before initiating sample collec-tions.16,20 The lack of washout did not appear to

impact the results because the order in which the dietswere fed did not have a significant effect on the results.

Cats had poor FS at the beginning of the study whichimproved after consuming the therapeutic diets, but

based on the study design it is not possible to differen-tiate the effects of diet on the microbiome from theeffects of improvement in diarrhea.

Conclusions

In this study, we quantified variations in the compo-sition of hindgut microbiota in cats with chronic diar-

rhea and after clinical improvement while being fed 2

therapeutic diets. The bacterial phyla Firmicutes andBacteroidetes each comprised 3034% of all sequencesin this study, which is less for Firmicutes and more for

Bacteroidetes compared to most other studies. It is notknown if the differences are because of methodology

or because of health or dietary effects. Those bacteriawith the strongest correlation with FS included Corio-

bacteriaceae Slackia spp., Campylobacter upsaliensis,Enterobacteriaceae Raoultella spp., Coriobacteriaceae

Collinsella spp., and bacteria of unidentified generawithin the families of Clostridiales Lachnospiracea and

Aeromonadales Succinivibrionacease, suggesting thatincreased numbers of these organisms may be impor-

tant to gut health. Finally, from our data we cannot

conclude if these changes in the microbiome causedthe improvement in diarrhea or were a result of either

the diet or the improved fecal quality.

Footnotes

a Baseline maintenance diet: Fancy Feast Savory Salmon Feast

Cat food, Nestle Purina PetCare Company, St. Louis, MOb Diet X: Hills Prescription Diet i/d Feline, Hills Pet Nutri-

tion Inc, Topeka, KSc Diet Y: Purina Veterinary Diets EN Gastroenteric brand

Feline Formula, Nestle Purina PetCare Companyd Proteinase K: Fisher BioReagents # BP1700-500, Fisher Scien-

tific, Pittsburg, PAe Quant-It: Invitrogen Quant-It kit, dsDNA, broad range, Life

Technologies, Grand Island, NYf Core for Applied Genomics and Ecology, University of

Nebraska-Lincoln, Lincoln, NEg GeneTools image analysis software, Syngene, Frederick, MDh PicoGreen: Invitrogen, Life Technologiesi Qubit fluorometerg: Invitrogen, Life Technologiesj Roche-454 GS-FLX Pyrosequencer: 454 Life Sciences, a Roche

company, Branford, CTk SIMCA-P+, verion 12.0.1.0: Umetrics AB, Umea, Swedenl MATLAB: MathWorks Inc, Natick, MA

Acknowledgments

The authors thank the staff veterinarians and techni-cians at the Nestle Purina Petcare Center for assistance

with data collection and animal care. This study wassupported by the Nestle Research Center St. Louis.

Conflict of Interest: This project was funded and

conducted by Nestle Purina PetCare, St. Louis, MO

63164. All authors are full-time employees of this com-pany.

References

1. Bell JA, Kopper JJ, Turnbull JA, et al. Ecological charac-

terization of the colonic microbiota of normal and diarrheic

dogs. Interdiscip Perspect Infect Dis 2008;2008:149694. doi: 10.

1155/2008/149694.

2. Blaut M. Relationship of prebiotics and food to intestinal

microflora. Eur J Nutr 2002;41(Suppl. 1):I11I16.

3. Blaut M, Clavel T. Metabolic diversity of the intestinal

microbiota: Implications for health and disease. J Nutr 2007;137:

751S755S.

4. deVrese M, Marteau PR. Probiotics and prebiotics: Effects

on diarrhea. J Nutr 2007;137:803S811S.

5. Johnston KL, Lamport A, Ballevre O, Batt RM. A com-

parison of endoscopic and surgical collection procedures for the

analysis of the bacterial flora in duodenal fluid from cats. Vet J

1999;157:8589.

6. Inness VL, McCartney AL, Khoo C, et al. Molecular char-

acterisation of the gut microflora of healthy and inflammatory

bowel disease cats using fluorescence in situ hybridisation with

special reference to Desulfovibrio spp. J Anim Physiol Anim Nutr

(Berl) 2007;91:4853.

7. Johnston KL, Swift NC, Forster-van HM, et al. Compari-

son of the bacterial flora of the duodenum in healthy cats and

cats with signs of gastrointestinal tract disease. J Am Vet Med

Assoc 2001;218:48

51.8. Janeczko S, Atwater D, Bogel E, et al. The relationship of

mucosal bacteria to duodenal histopathology, cytokine mRNA,

and clinical disease activity in cats with inflammatory bowel dis-

ease. Vet Microbiol 2008;128:178193.

9. Krecic MR. Feline inflammatory bowel disease: Treatment,

prognosis, and new developments. Compendium 2001;23:964973.

10. Zentek J, Marquart B, Pietrzak T, et al. Dietary effects on

bifidobacteria and Clostridium perfringens in the canine intestinal

tract. J Anim Physiol Anim Nutr (Berl) 2003;87:397407.

11. Barry KA, Wojcicki BJ, Middelbos IS, et al. Dietary cellu-

lose, fructooligosaccharides, and pectin modify fecal protein ca-

tabolites and microbial populations in adult cats. J Anim Sci

2010;88:29782987.

12. Middelbos IS, Vester Boler BM, Qu A, et al. Phylogenetic

characterization of fecal microbial communities of dogs fed dietswith or without supplemental dietary fiber using 454 pyrosequenc-

ing. PLoS ONE 2010;5:e9768. doi:10.1371/journal.pone.0009768.

13. Hooda A, Vester-Boler BM, Kerr KR, et al. The gut

microbiome of kittens is affected by dietary protein:carbohydrate

ratio and associated with blood metabolite and hormone concen-

trations. Brit J Nutr 2013 May;109(9):16371646. doi:10.1017/

S000711451200 3479. Epub 2012 Aug 31.

14. Marks SL, Laflamme DP, McAloose D. Dietary trial

using a commercial hypoallergenic diet containing hydrolyzed

protein for dogs with inflammatory bowel disease. Vet Ther

2002;3:109118.

15. Guilford WG, Jones BR, Markwell PJ, et al. Food sen-

sitivity in cats with chronic idiopathic gastrointestinal problems.

J Vet Intern Med 2001;15:713.

64 Ramadan et al


7/7

16. Laflamme DP, Long GM. Evaluation of two diets in the

nutritional management of cats with naturally occurring chronic

diarrhea. Vet Ther 2004;5:4351.

17. Allenspach K, Wieland B, Grone A, Gaschen F. Chronic

enteropathies in dogs: Evaluation of risk factors for negative out-

come. J Vet Intern Med 2007;21:700708.

18. Mandigers PJ, Biourge V, van den Ingh TS, et al. A ran-

domized, open-label, positively-controlled field trial of a hydro-

lyzed protein diet in dogs with chronic small bowl enteropathy. J

Vet Intern Med 2010;24:13501357.

19. Laflamme DP, Xu H, Long G. Effect of diets differing in

fat content on chronic diarrhea in cats. J Vet Intern Med

2011;25:230235.

20. Laflamme DP, Xu H, Cupp CJ, et al. Evaluation of canned

therapeutic diets for the management of cats with naturally

occurring chronic diarrhea. J Fel Med Surg 2012;14:669677.

21. Martineau B, Laflamme DP, Jones W, Jones J. Effect of

diet on markers of intestinal health in dogs. Res Vet Sci

2002;72:223227.

22. Wakshlag JJ, Simpson KW, Struble AM, Dowd SE. Neg-

ative fecal characteristics are associated with pH and fecal flora

alterations during dietary change in dogs. Intern J Appl Res Vet

Med 2011;9:278

283.23. Swanson KS, Suchodolski JS, Turnbaugh PJ. Companion

animals symposium: Microbes and health. J Anim Sci

2011;89:14961497.

24. Suchodolski JS. Companion animals symposium:

Microbes and gastrointestinal health of dogs and cats. J Anim

Sci 2011;89:15201530.

25. Ritchie LE. Molecular Characterization of the Intestinal

Bacteria in Healthy Cats and a Comparison of the Fecal Bacte-

rial Flora Between Healthy Cats and Cats with Inflammatory

Bowel Disease (IBD). College Station, TX: Texas A&M Univer-

sity; 2008. Masters of Science Thesis.

26. Ritchie LE, Burke KF, Garcia-Mazcorro JF, et al. Char-

acterization of fecal microbiota in cats using universal 16S rRNA

gene and group-specific primers for Lactobacillus and Bifidobacte-

rium spp. Vet Microbiol 2010;144:140146.27. Tsai YL, Olson BH. Rapid method for direct extraction

of DNA from soil and sediments. Appl Environ Microbiol

1991;57:10701074.

28. Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME

allows analysis of high-throughput community sequencing data.

Nat Methods 2010;7:335336.

29. Edgar RC. Search and clustering orders of magnitude fas-

ter than BLAST. Bioinformatics 2010;26:24602461.

30. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayes-

ian classifier for rapid assignment of rRNA sequences into the

new bacterial taxonomy. Appl Environ Microbiol 2007;73:

52615267.

31. Trygg J, Holmes E, Lundstedt T. Chemometrics in metab-

onomics. J Proteome Res 2007;6:469479.

32. Trygg J, Wold S. Orthogonal projections to latent struc-

tures (OPLS). J Chemom 2002;16:119128.

33. Garcia-Mazcorro JF, Lanerie DJ, Dowd SE, et al. Effect

of a multi-species synbiotic formulation on fecal bacterial micro-

biota of healthy cats and dogs as evaluated by pyrosequencing.

FEMS Microbiol 2011;78:542554.

34. Handl S, Dowd SE, Garcia-Mazcorro JF, et al. Massive

parallel 16S rRNA gene pyrosequencing reveals highly diverse

fecal bacterial and fungal communities in healthy dogs and cats.

FEMS Microbiol Ecol 2011;76:301310.

35. Tun HM, Brar MS, Khin N, et al. Gene-centric metage-

nomics analysis of feline intestinal microbiome using 454 junior

pyrosequencing. J Microbiol Methods 2012;88:369376.

36. Dethlefsen L, Huse S, Sogin ML, Relman DA. The perva-

sive effects of an antibiotic on the human gut microbiota, as

revealed by deep 16S rRNA sequencing. PLoS Biol 2008;6:e280.

doi:10.1371/journal.pbio.0060280.

37. Wolf M, Muller T, Dandekar T, Pollack JD. Phylogeny of

Firmicutes with special reference to Mycoplasma (Mollicutes) as

inferred from phosphoglycerate kinase amino acid sequence data.

Int J Syst Evol Microbiol 2004;54:871

875.38. Ludwig W, Schleifer KH, Whitman WB. Revised road

map to the phylum Firmicutes. In: DeVos P, Garrity G, Jones

D, Krieg NR, Ludwig W, Rainey FA, Schleifer KH, Whitman

WB (eds). Bergeys Manual of Systemic Bacteriology, volume 3,

2nd ed. New York: Springer-Verlag; 2009:113.

39. Lubbs DC, Vester BM, Fastinger ND, Swanson KS. Die-

tary protein concentration affects intestinal microbiota of adult

cats: A study using DGGE and qPCR to evaluate differences in

microbial populations in the feline gastrointestinal tract. J Anim

Physiol Anim Nutr (Berl) 2009;93:113121.

Supporting Information

Additional Supporting Information may be found inthe online version of this article:

Table S1. Fecal microbiota, determined by 454pyrosequencing that were significantly (P < 0.05) cor-

related with fecal scoresa from cats with naturallyoccurring chronic diarrhea after being fed therapeutic

diet Diet Xb or Diet Yc for 4 weeks. Bacteria are

sorted according to their phylum.


Fecal Microbiota of Cats With Naturally Occurring Chronic Diarrhea

Documents

Transcript of Fecal Microbiota of Cats With Naturally Occurring Chronic Diarrhea